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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci Chethan Pandarinath Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago and Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill and NC State University and UNC Chapel Hill Neuroscience Center and NC State/UNC Closed-Loop Engineering for Advanced Rehabilitation (CLEAR) Coulter Dept. of Biomedical Engineering Emory University and Georgia Tech and Dept. of Neurosurgery Emory University

ISBN: (纸本)9781713845393

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data.

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Heterogeneous data integration methods for patient similarity networks

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BRIEFINGS IN BIOINFORMATICS 2022年第4期23卷 bbac207-bbac207页

作者： Gliozzo, Jessica Mesiti, Marco Notaro, Marco Petrini, Alessandro Patak, Alex Puertas-Gallardo, Antonio Paccanaro, Alberto Valentini, Giorgio Casiraghi, Elena Univ Milan Comp Sci Collaborat Doctoral Partnership Program Milan Italy European Commiss Joint Res Ctr Brussels Belgium Univ Milan Dept Comp Sci Giovanni Degli Antoni Milan Italy Milan Univ Comp Sci Dept Milan Italy Univ Milan Dept Comp Sci Milan Italy AnacletoLab Lab Bioinformat & Computat Biol Milan Italy Inst Municipal Invest Med Barcelona Spain Joint Res Ctr Three Dimens Med Imaging Ispra Italy Inst Hlth & Consumer Protect Mol Biol & Genom Unit Bioinformat Team Ispra Italy Knowledge Hlth & Consumer Safety Ispra Italy European Commiss Joint Res Ctr JRC Brussels Belgium Fundacao Getulic Vargas Sch Appl Math Machine Learning & Computat Biol Rio De Janeiro Brazil Royal Holloway Univ London Dept Comp Sci London England Royal Holloway Univ London Ctr Syst & Synthet Biol London England Univ Milan UNIMI Dept Comp Sci Milan Italy European Union Joint Res Ctr UNIMI European Doctorate Geno Yus & Bioinformat Milan Italy Univ Milan AnacletoLab Computat Biol & Bioinfmmat Lab Dept Comp Sci Milan Italy Univ Milan Dept Comp Sci Giovanni Antoni Milan Italy Univ Milan AnacletoLab Computat Biol & Bioinformat Lab Dept Comp Sci Milan Italy

Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms. The advent of high-throughput technologies, enabling us to acquire high-dimensional views of the same patients (e.g. omics data, laboratory data, imaging data), calls for the development of data fusion techniques for PSNs in order to leverage this rich heterogeneous information. In this article, we review existing methods for integrating multiple biomedical data views to construct PSNs, together with the different patient similarity measures that have been proposed. We also review methods that have appeared in the machine learning literature but have not yet been applied to PSNs, thus providing a resource to navigate the vast machine learning literature existing on this topic. In particular, we focus on methods that could be used to integrate very heterogeneous datasets, including multi-omics data as well as data derived from clinical information and medical imaging.

关键词： patient similarity networks biomedical applications multimodal data data fusion

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Construction of integrated microRNA and mRNA immune cell signatures to predict survival of patients with breast and ovarian cancer

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GENES CHROMOSOMES & CANCER 2019年第1期58卷 34-42页

作者： Ray, Mondira Ruffalo, Matthew M. Bar-Joseph, Ziv Howard Hughes Med Inst Med Res Fellows Program Chevy Chase MD USA Univ Pittsburgh Sch Med Pittsburgh PA USA Carnegie Mellon Univ Sch Comp Sci Dept Computat Biol 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Sch Comp Sci Dept Machine Learning 5000 Forbes Ave Pittsburgh PA 15213 USA

In the tumor microenvironment, immune cells have emerged as key regulators of cancer progression. While much work has focused on characterizing tumor-related immune cells through gene expression profiling, microRNAs (miRNAs) have also been reported to regulate immune cells in the tumor microenvironment. Using regression-based computational methods, we have constructed for the first time, immune cell signatures based on miRNA expression from The Cancer Genome Atlas breast and ovarian cancer datasets. Combined with existing mRNA immune cell signatures, the integrated mRNA-miRNA leukocyte signatures are better able to delineate prognostic immune cell subsets within both cancers compared to the mRNA or miRNA signatures alone. Moreover, using the miRNA signatures, the anti-inflammatory M2 macrophages emerged as the most significantly prognostic cell type in the breast cancer data (HR [hazard ratio]: 12.9;CI [confidence interval]: 3.09-52.9;P = 4.22E-4), whereas the pro-inflammatory M1 macrophages emerged as the most prognostic immune cell type in the ovarian cancer data (HR: 0.2;CI: 0.04-0.56, P = 5.02E-3). These results suggest that our integrated miRNA and mRNA leukocyte signatures could be used to better delineate prognostic leukocyte subsets within cancers, whereas continued investigation may further support the regulatory relationships predicted between the miRNAs and immune cells found within our signature matrices.

关键词： cancer genomics cancer immunology computational biology miRNA immune cell signatures tumor microenvironment tumor-associated macrophages

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The Transformation of Patient-Clinician Relationships with AI-based Medical Advice

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COMMUNICATIONS OF THE ACM 2021年第3期64卷 46-48页

作者： Nov, Oded Aphinyanaphongs, Yindalon Lui, Yvonne W. Mann, Devin Porfiri, Maurizio Riedl, Mark Rizzo, John-Ross Wiesenfeld, Batia NYU Tandon Sch Engn Human Comp Interact New York NY 10016 USA NYU Langone Hlth Operat Data Sci & Machine Learning New York NY USA NYU Grossman Sch Med Healthcare Delivery Sci New York NY USA NYU Grossman Sch Med Dept Radiol New York NY USA Grossman Sch Med Populat Hlth New York NY USA NYU Langone Hlth Informat Innovat New York NY USA NYU Tandon Sch Engn Mech & Aerosp Engn & Biomed Engn New York NY USA Georgia Inst Technol Comp Sci Sch Interact Comp Atlanta GA 30332 USA NYU Grossman Sch Med Innovat & Technol Dept Rehabil Med New York NY USA NYU Grossman Sch Med Rehabil Med & Neurol New York NY USA NYU Tandon Sch Engn Biomed & Mech Engn New York NY USA NYU Stern Sch Business Management New York NY USA NYU Stern Sch Business Business & Soc Program New York NY USA

A "bring your own algorithm" era in healthcare.

关键词： Insulin

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Liver metastasis restrains immunotherapy efficacy via macrophage-mediated T cell elimination

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NATURE MEDICINE 2021年第1期27卷 152-+页

作者： Yu, Jiali Green, Michael D. Li, Shasha Sun, Yilun Journey, Sara N. Choi, Jae Eun Rizvi, Syed Monem Qin, Angel Waninger, Jessica J. Lang, Xueting Chopra, Zoey El Naqa, Issam Zhou, Jiajia Bian, Yingjie Jiang, Long Tezel, Alangoya Skvarce, Jeremy Achar, Rohan K. Sitto, Merna Rosen, Benjamin S. Su, Fengyun Narayanan, Sathiya P. Cao, Xuhong Wei, Shuang Szeliga, Wojciech Vatan, Linda Mayo, Charles Morgan, Meredith A. Schonewolf, Caitlin A. Cuneo, Kyle Kryczek, Ilona Ma, Vincent T. Lao, Christopher D. Lawrence, Theodore S. Ramnath, Nithya Wen, Fei Chinnaiyan, Arul M. Cieslik, Marcin Alva, Ajjai Zou, Weiping Univ Michigan Dept Surg Ann Arbor MI 48109 USA Univ Michigan Michigan Rogel Canc Ctr Ctr Excellence Canc Immunol & Immunotherapy Ann Arbor MI USA Univ Michigan Dept Radiat Oncol Ann Arbor MI 48109 USA Vet Affairs Ann Arbor Healthcare Syst Ann Arbor MI USA Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA Univ Michigan Dept Biostat Ann Arbor MI USA Univ Michigan Sch Med Ann Arbor MI USA Univ Michigan Dept Pathol Ann Arbor MI 48109 USA Univ Michigan Michigan Ctr Translat Pathol Ann Arbor MI 48109 USA Univ Michigan Chem Engn Ann Arbor MI 48109 USA Univ Michigan Dept Internal Med Div Hematol Oncol Ann Arbor MI 48109 USA H Lee Moffitt Canc Ctr & Res Inst Machine Learning Dept Tampa FL USA Univ Michigan Sch Publ Hlth Ann Arbor MI 48109 USA Univ Michigan Howard Hughes Med Inst Ann Arbor MI 48109 USA Univ Michigan Sch Med Grad Program Immunol Ann Arbor MI USA Univ Michigan Sch Med Grad Program Canc Biol Ann Arbor MI USA

Metastasis is the primary cause of cancer mortality, and cancer frequently metastasizes to the liver. It is not clear whether liver immune tolerance mechanisms contribute to cancer outcomes. We report that liver metastases diminish immunotherapy efficacy systemically in patients and preclinical models. Patients with liver metastases derive limited benefit from immunotherapy independent of other established biomarkers of response. In multiple mouse models, we show that liver metastases siphon activated CD8(+) T cells from systemic circulation. Within the liver, activated antigen-specific Fas(+)CD8(+) T cells undergo apoptosis following their interaction with FasL(+)CD11b(+)F4/80(+) monocyte-derived macrophages. Consequently, liver metastases create a systemic immune desert in preclinical models. Similarly, patients with liver metastases have reduced peripheral T cell numbers and diminished tumoral T cell diversity and function. In preclinical models, liver-directed radiotherapy eliminates immunosuppressive hepatic macrophages, increases hepatic T cell survival and reduces hepatic siphoning of T cells. Thus, liver metastases co-opt host peripheral tolerance mechanisms to cause acquired immunotherapy resistance through CD8(+) T cell deletion, and the combination of liver-directed radiotherapy and immunotherapy could promote systemic antitumor immunity. Liver metastases reduce clinical and preclinical immune-checkpoint inhibitor efficacy through hepatic siphoning of circulating activated CD8(+) T cells, but therapeutic benefit can be improved by combining immunotherapy with liver-directed radiotherapy.

关键词： Cancer immunotherapy Metastasis Monocytes and macrophages T cells Tumour immunology

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Nuclear Neural Networks: Emulating Late Burning Stages in Core Collapse Supernova Progenitors

arXiv

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arXiv 2025年

作者： Grichener, Aldana Renzo, Mathieu Kerzendorf, Wolfgang E. Farmer, Rob de Mink, Selma E. Bellinger, Earl Patrick Chan, Chi-Kwan Chen, Nutan Farag, Ebraheem Justham, Stephen Steward Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Max Planck Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching85748 Germany Department of Physics Technion Haifa3200003 Israel Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Ludwig-Maximilians-Universitat Munchen Geschwister-Scholl-Platz 1 Munchen80539 Germany Department of Astronomy Yale University New HavenCT06511 United States Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 North Santa Rita TucsonAZ85721 United States Machine Learning Research Lab Volkswagen AG Munich38440 Germany

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully-coupled stiff ordinary differential equations (ODEs), making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof-of-concept to evaluate its performance. The NNN takes the temperature, density and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280 − 660 % and the nuclear energy generation by 250 − 750 %, consistently outperforming the small network across all timesteps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100 − 106 %. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN trained models into stellar evolution codes is promising for facilitating large-scale generation of core-collapse supernova (CCSN) progenitors with higher physical fidelity. © 2025, CC BY.

关键词： Nucleosynthesis

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Beyond Ultra-diffuse Galaxies. I. Mass–Size Outliers among the Satellites of Milky Way Analogs

arXiv

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arXiv 2022年

作者： Li, Jiaxuan Greene, Jenny E. Greco, Johnny P. Huang, Song Melchior, Peter Beaton, Rachael Casey, Kirsten Danieli, Shany Goulding, Andy Joseph, Remy Kado-Fong, Erin Kim, Ji Hoon MacArthur, Lauren A. Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States The Ohio State University ColumbusOH43210 United States Department of Astronomy Tsinghua Center for Astrophysics Tsinghua University Beijing100084 China Center for Statistics & Machine Learning Princeton University PrincetonNJ08544 United States Oskar Klein Centre for Cosmoparticle Physics Department of Physics Stockholm University StockholmSE-106 91 Sweden Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

Large diffuse galaxies are hard to find, but understanding the environments where they live, their numbers, and ultimately their origins, is of intense interest and importance for galaxy formation and evolution. Using Subaru’s Hyper Suprime-Cam Strategic Survey program, we perform a systematic search for low surface brightness galaxies and present novel and effective methods for detecting and modeling them. As a case study, we surveyed 922 Milky Way analogs in the nearby Universe (0.01 UPG = 0.31 ± 0.05 ultra-puffy galaxies on average, which is consistent with but slightly lower than the observed abundance at this halo mass in the Local Volume. We also construct a sample of ultra-diffuse galaxies (UDGs) in MW analogs and find an abundance of NUDG = 0.44 ± 0.05 per host. With literature results, we confirm that the UDG abundance scales with the host halo mass following a sublinear power law. We argue that our definition for ultra-puffy galaxies, which is based on the mass–size relation, is more physically motivated than the common definition of ultra-diffuse galaxies, which depends on the surface brightness and size cuts and thus yields different surface mass density cuts for quenched and star-forming galaxies. © 2022, CC BY.

关键词： Galaxies

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Position: Bayesian Deep learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on machine learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive learning

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The SCUBA-2 Large eXtragalactic Survey: 850μm map, catalogue and the bright-end number counts of the XMM-LSS field

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2023年第3期520卷 3669-3687页

作者： Garratt, T. K. Geach, J. E. Tamura, Y. Coppin, K. E. K. Franco, M. Ao, Y. Chen, C. -C Cheng, C. Clements, D. L. Dai, Y. S. Dannerbauer, H. Greve, T. R. Hatsukade, B. Hwang, H. S. Jiang, L. Kohno, K. Koprowski, M. P. Michalowski, M. J. Sawicki, M. Scott, D. Shim, H. Takeuchi, T. T. Wang, W. -H Xue, Y. Q. Yang, C. Univ Hertfordshire Ctr Astrophys Res Hatfield AL10 9AB England Nagoya Univ Div Particle & Astrophys Sci Nagoya Aichi 4648602 Japan Chinese Acad Sci Key Lab Radio Astron Purple Mt Observ 10 Yuanhua Rd Nanjing 210023 Peoples R China Univ Sci & Technol China Sch Astron & Space Sci Hefei 230026 Anhui Peoples R China Acad Sinica Sect 4 Inst Astron & Astrophys 1 Roosevelt Rd Taipei 10617 Taiwan Chinese Acad Sci Natl Astron Observ South Amer Ctr Astron Beijing 100101 Peoples R China Imperial Coll London Blackett Lab Prince Consort Rd London SW7 2AZ England Chinese Acad Sci Natl Astron Observ 20A Datun Rd Beijing 100101 Peoples R China Inst Astrofis Canarias IAC E-38205 Tenerife Spain Univ Laguna Dept Astrofis E-38206 Tenerife Spain Tech Univ Denmark Natl Space Inst DTU Space Elektrovej 327 DK-2800 Lyngby Denmark UCL Dept Phys & Astron Gower St London WC1E 6BT England Univ Tokyo Inst Astron Grad Sch Sci 2-21-1 Osawa Mitaka Tokyo 1810015 Japan Seoul Natl Univ Dept Phys & Astron Astron Program 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ SNU Astron Res Ctr Astron Program 1 Gwanak Ro Seoul 08826 South Korea Peking Univ Kavli Inst Astron & Astrophys 5 Yiheyuan Rd Beijing 100871 Peoples R China Nicolaus Copernicus Univ Inst Astron Fac Phys Astron & Informat Grudziadzka 5 PL-87100 Torun Poland Adam Mickiewicz Univ Astron Observ Inst Fac Phys ul Sloneczna 36 PL-60286 Poznan Poland St Marys Univ Inst Computat Astrophys Dept Astron & Phys Halifax NS B3H 3C3 Canada Univ British Columbia Dept Phys & Astron Vancouver BC Canada Kyungpook Natl Univ Dept Earth Sci Educ Daegu 41566 South Korea Inst Stat Math Res Ctr Stat Machine Learning 10-3 Midori Cho Tachikawa Tokyo 1908562 Japan Univ Sci & Technol China Dept Astron CAS Key Lab Res Galaxies & Cosmol Hefei 230026 Peoples R China Univ Technol Dept Space Earth & Environm Chalmers Gothenburg Sweden

We present 850 mu m imaging of the XMM-LSS field observed for 170 h as part of the James Clerk Maxwell Telescope SCUBA-2 Large eXtragalactic Survey (S2LXS). S2LXS XMM-LSS maps an area of 9deg(2) reaching a moderate depth of 1 sigma?4mJy beam(-1) 1J?4mJy beam(-1?). This is the largest contiguous area of extragalactic sky mapped by James Clerk Maxwell Telescope (JCMT) at 850 mu m to date. The wide area of the S2LXS XMM-LSS survey allows us to probe the ultra-bright (?S-850 mu m?15mJy ), yet rare submillimetre population. We present the S2LXS XMM-LSS catalogue, which comprises 40 sources detected at > 5 sigma significance, with deboosted flux densities in the range of 77-48mJy48mJy?. We robustly measure the bright-end of the 850 mu m number counts at flux densities > 7mJy > 7mJy?, reducing the Poisson errors compared to existing measurements. The S2LXS XMM-LSS observed number counts show the characteristic upturn at bright fluxes, expected to be motivated by local sources of submillimetre emission and high-redshift strongly lensed galaxies. We find that the observed 850 mu m number counts are best reproduced by model predictions that include either strong lensing or source blending from a 15-arcsec beam, indicating that both may make an important contribution to the observed overabundance of bright single dish 850 mu m selected sources. We make the S2LXS XMM-LSS 850 mu m map and > 5 sigma catalogue presented here publicly available.

关键词： catalogues surveys galaxies:high redshift submillimetre:galaxies

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Genome-wide transcription factor-binding maps reveal cell-specific changes in the regulatory architecture of human HSPCs

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BLOOD 2023年第17期142卷 1448-1462页

作者： Subramanian, Shruthi Thoms, Julie A. I. Huang, Yizhou Cornejo-Paramo, Paola Koch, Forrest C. Jacquelin, Sebastien Shen, Sylvie Song, Emma Joshi, Swapna Brownlee, Chris Woll, Petter S. Chacon-Fajardo, Diego Beck, Dominik Curtis, David J. Yehson, Kenneth Antonenas, Vicki O'Brien, Tracey Trickett, Annette Powell, Jason A. Lewis, Ian D. Pitson, Stuart M. Gandhi, Maher K. Lane, Steven W. Vafaee, Fatemeh Wong, Emily S. Goettgens, Berthold Alinejad-Rokny, Hamid Wong, Jason W. H. Pimanda, John E. Univ New South Wales Sch Clin Med Sydney Australia Univ New South Wales Sch Biomed Sci Sydney Australia Univ Technol Sydney Sch Biomed Engn Ctr Hlth Technol Sydney Australia Victor Chang Cardiac Res Inst Sydney Australia Univ New South Wales Fac Sci Sch Biotechnol & Biomol Sci Sydney Australia Macrophage Biol Lab Mater Res Brisbane Australia Prince Wales Hosp Bone Marrow Transplant Lab NSW Hlth Pathol Randwick NSW Australia Univ New South Wales Mark Wainwright Analyt Ctr Sydney Australia Karolinska Inst Ctr Hematol & Regenerat Med Dept Med Huddinge Sweden Monash Univ Australian Ctr Blood Dis Melbourne Vic Australia NSW Hlth Pathol Blood Transplant & Cell Therapies Lab Westmead NSW Australia Sydney Childrens Hosp Sydney Australia Univ South Australia Ctr Canc Biol SA Pathol Adelaide Australia Univ Adelaide Adelaide Med Sch Adelaide Australia Univ Queensland Blood Canc Res Grp Mater Res Brisbane Qld Australia QIMR Berghofer Med Res Canc Program Brisbane Australia Univ New South Wales UNSW Data Sci Hub Sydney Australia Wellcome MRC Cambridge Stem Cell Inst Cambridge England Univ New South Wales Grad Sch Biomed Engn Biomed Machine Learning Lab Sydney Australia Univ Hong Kong Li Ka Shing Fac Med Sch Biomed Sci Hong Kong Peoples R China Prince Wales Hosp Haematol Dept Sydney Australia Univ New South Wales Lowy Canc Res Ctr Level 2 Sydney NSW 2052 Australia Pokfulam L1-51Lab Block 21 Sassoon Rd Hong Kong Peoples R China

Hematopoietic stem and progenitor cells (HSPCs) rely on a complex interplay among transcription factors (TFs) to regulate differentiation into mature blood cells. A heptad of TFs (FLI1, ERG, GATA2, RUNX1, TAL1, LYL1, LMO2) bind regulatory elements in bulk CD34(+) HSPCs. However, whether specific heptad-TF combinations have distinct roles in regulating hematopoietic differentiation remains unknown. We mapped genome-wide chromatin contacts (HiC, H3K27ac, HiChIP), chromatin modifications (H3K4me3, H3K27ac, H3K27me3) and 10 TF binding profiles (heptad, PU.1, CTCF, STAG2) in HSPC subsets (stem/multipotent progenitors plus common myeloid, granulocyte macrophage, and megakaryocyte erythrocyte progenitors) and found TF occupancy and enhancer-promoter interactions varied significantly across cell types and were associated with cell-type-specific gene expression. Distinct regulatory elements were enriched with specific heptad-TF combinations, including stem-cell-specific elements with ERG, and myeloid-and erythroid-specific elements with combinations of FLI1, RUNX1, GATA2, TAL1, LYL1, and LMO2. Furthermore, heptad-occupied regions in HSPCs were subsequently bound by lineage-defining TFs, including PU.1 and GATA1, suggesting that heptad factors may prime regulatory elements for use in mature cell types. We also found that enhancers with cell-type-specific heptad occupancy shared a common grammar with respect to TF binding motifs, suggesting that combinatorial binding of TF complexes was at least partially regulated by features encoded in DNA sequence motifs. Taken together, this study comprehensively characterizes the gene regulatory landscape in rare subpopulations of human HSPCs. The accompanying data sets should serve as a valuable resource for understanding adult hematopoiesis and a framework for analyzing aberrant regulatory networks in leukemic cells.

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